Correlation between Saliva and Plasma Levels of Endothelin Isoforms ET-1, ET-2, and ET-3
Bibliographic record
Abstract
Although saliva endothelins are emerging as valuable noninvasive cardiovascular biomarkers, reports on the relationship between isoforms in saliva and plasma remain scarce. We measured endothelins in concurrent saliva and plasma samples (n = 30 males; age 18-63) by HPLC-fluorescence. Results revealed statistically significant positive correlations among all isoforms between saliva and plasma: big endothelin-1 (BET-1, 0.55 ± 0.27 versus 3.35 ± 1.28 pmol/mL; r = 0.38, p = 0.041), endothelin-1 (ET-1, 0.52 ± 0.21 versus 3.45 ± 1.28 pmol/mL; r = 0.53, p = 0.003), endothelin-2 (ET-2, 0.21 ± 0.07 versus 1.63 ± 0.66 pmol/mL; r = 0.51, p = 0.004), and endothelin-3 (ET-3, 0.39 ± 0.19 versus 2.32 ± 1.44 pmol/mL; r = 0.75, p < 0.001). Correlations of BET-1, ET-1, and ET-3 within each compartment were positive in both plasma (p < 0.05) and saliva (p ≤ 0.1), whereas ET-2 was not significantly correlated with other isoforms in either plasma or saliva. For all isoforms, concentrations varied on average fivefold between individuals (90th/10th percentiles); individuals with high plasma endothelin levels generally had high saliva endothelin levels. Our results reveal that salivary ET isoform profiles portray the plasmatic profiles and support the view of coordinated regulation of ET-1 and ET-3, but distinct regulatory pathways for ET-2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".